Nowadays, there is a mobile application for almost every need, for instance, training, games, and news applications. Almost all the companies have also their own mobile applications that facilitate the daily communication between their employees and supervisors. In this thesis, the development process of an Android application for the company Askås is created, where the employees can for example send a Flex application or sick leave application, see their registered personal information and edit them. The final result of the project is almost what Askås asked for, An Android application that facilitates their employees’ daily needs. The development process, the results, and the encountered problems are also represented in a more detailed manner in the thesis. 

Cellular Internet of Things (CIoT) has emerged as a promising technology to support applications that generate infrequent data. One requirement on these applications, often battery-powered devices, is low energy consumption to enable extended battery life. Narrowband IoT (NB-IoT) is a promising technology for IoT due to its low power consumption, which is essential for devices that need to run on battery power for extended periods. However, the current battery life of NB-IoT devices is only a few years, which is insufficient for many applications. This thesis investigates the impact of energy-saving mechanisms standardized by 3GPP on battery life of NB-IoT devices. The main research objective is to classify and analyze existing energy-saving solutions for CIoT and examine their limitations, to study the impact of standardized energy-saving mechanisms on the battery life of NB-IoT devices, both in isolation and combined, and to provide guidelines on how to configure NB-IoT devices to reduce energy consumption efficiently. The research aims to provide a deeper understanding of the effect of energy-saving mechanisms and best practices to balance energy efficiency and performance of NB-IoT devices. Applying the proposed solutions makes it possible to achieve a battery life of 10~years or more for CIoT devices.

Cellular Internet of Things (CIoT) is a Low-Power Wide-Area Network (LPWAN) technology. It aims for cheap, lowcomplexity IoT devices that enable large-scale deployments and wide-area coverage. Moreover, to make large-scale deployments of CIoT devices in remote and hard-to-access locations possible, a long device battery life is one of the main objectives of these devices. To this end, 3GPP has defined several energysaving mechanisms for CIoT technologies, not least for the Narrow-Band Internet of Things (NB-IoT) technology, one of the major CIoT technologies. Examples of mechanisms defined include CONNECTED-mode DRX (cDRX), Release Assistance Indicator (RAI), and Power Saving Mode (PSM). This paper considers the impact of the essential energy-saving mechanisms on minimizing the energy consumption of NB-IoT devices, especially the cDRX and RAI mechanisms. The paper uses a purpose-built NB-IoT simulator that has been tested in terms of its built-in energy-saving mechanisms and validated with realworld NB-IoT measurements. The simulated results show that it is possible to save 70%-90% in energy consumption by enabling the cDRX and RAI. In fact, the results suggest that a battery life of 10 years is only achievable provided the cDRX, RAI, and PSM energy-saving mechanisms are correctly configured and used

This paper studies the impact of tunable parametersin the NB-IoT stack on the energy consumption of a user equipment(UE), e.g., a wireless sensor. NB-IoT is designed to enablemassive machine-type communications for UE while providing abattery lifetime of up to 10 years. To save battery power, most oftime the UE is in dormant state and unreachable. Still, duringthe CONNECTED and IDLE state, correct tuning of criticalparameters, like Discontinuous reception (DRX), and extendedDiscontinuous reception (eDRX), respectively, are essential to savebattery power. Moreover, the DRX and eDRX actions relate tovarious parameters which are needed to be tuned in order toachieve a required UE battery lifetime. The objective of thispaper is to observe the influence of an appropriate tuning ofthese parameters to reduce the risk of an early battery drainage

In this paper, we study the energy consumptionof Narrowband IoT devices. The paper suggests that key tosaving energy for NB-IoT devices is the usage of full Discontinuous Reception (DRX), including the use of connected-mode DRX (cDRX): In some cases, cDRX reduced the energy consumption over a 10-year period with as much as 50%. However, the paper also suggests that tunable parameters, such as the inactivity timer, do have a significant impact. On the basis of our findings, guidelines are provided on how to tune the NB-IoT device so that it meets the target of the 3GPP, i.e., a 5-Wh battery should last for at least 10 years. It is further evident from our results that the energy consumption is largely dependent on the intensity and burstiness of the traffic, and thus could be significantly reduced if data is sent in bursts with less intensity,irrespective of cDRX support.

Deploying Cellular Internet of Things (CIoT) devices in urban and remote areas faces significant energy efficiency challenges. This is especially true for Narrowband IoT (NB-IoT) devices, which are expected to function on a single charge for up to 10 years while transmitting small amounts of data daily. The 3rd Generation Partnership Project (3GPP) has introduced energy-saving mechanisms in Releases 13 to 16, including Early Data Transmission (EDT) and Preconfigured Uplink Resources (PURs). These mechanisms extend battery life and reduce latency by enabling data transmission without an active Radio Resource Control (RRC) connection or Random Access Procedure (RAP). This paper examines these mechanisms using the LENA-NB simulator in the ns-3 environment, which is a comprehensive framework for studying various aspects of NB-IoT. The LENA-NB has been extended with PURs, and our analysis shows that PURs significantly enhance battery life and latency efficiency, particularly in high-density environments. Compared to the default RAP method, PURs reduce energy consumption by more than 2.5 times and increases battery life by 1.6 times. Additionally, PURs achieve latency reductions of 2.5 - 3.5 times. The improvements with PURs are most notable for packets up to 125 bytes. Our findings highlight PURs' potential to enable more efficient and effective CIoT deployments across various scenarios. This study represents a detailed analysis of latency and energy consumption in a simulated environment, advancing the understanding of PURs' benefits.

The Cellular Internet of Things (CIoT), a new paradigm, paves the way for a large-scale deployment of IoT devices. CIoT promises enhanced coverage and massive deployment of low-cost IoT devices with an expected battery life of up to 10 years. However, such a long battery life can only be achieved provided the CIoT device is configured with energy efficiency in mind. This paper conducts a comprehensive survey on energy-saving solutions in 3GPP-based CIoT networks. In comparison to current studies, the contribution of this paper is the classification and an extensive analysis of existing energy-saving solutions for CIoT, e.g., the configuration of particular parameter values and software modifications of transport- or radio-layer protocols, while also stressing key parameters impacting the energy consumption such as the frequency of data reporting, discontinuous reception cycles (DRX), and Radio Resource Control (RRC) timers. In addition, we discuss shortcomings, limitations, and possible opportunities which can be investigated in the future to reduce the energy consumption of CIoT devices.

The Cellular Internet of Things (CIoT) has seen significant growth in recent years. With the deployment of 5G, it has become essential to reduce the power consumption of these devices for long-term sustainability. The upcoming 6G cellular network introduces the concept of zero-energy CIoT devices, which do not require batteries or manual charging. This paper focuses on these devices, providing insight into their feasibility and practical implementation. The paper examines how CIoT devices use simultaneous wireless information and power transfer, beamforming, and backscatter communication techniques. It also analyzes the potential use of energy harvesting and power management in zero-energy CIoT devices. Furthermore, the paper explores how low-power transceivers can lower energy usage while maintaining dependable communication functions.

With the aim to improve road safety services in critical situations, vehicle trajectory and future location prediction are important tasks. An infinite set of possible future trajectories can exit depending on the current state of vehicle motion. In this paper, we present a multimodel-based Extended Kalman Filter (EKF), which is able to predict a set of possible scenarios for vehicle future location. Five different EKF models are proposed in which the current state of a vehicle exists, particularly, a vehicle at intersection or on a curve path. EKF with Interacting Multiple Model framework is explored combinedly for mathematical model creation and probability calculation for that model to be selected for prediction. Three different parameters are considered to create a state vector matrix, which includes vehicle position, velocity, and distance of the vehicle from the intersection. Future location of a vehicle is then used by the software-defined networking controller to further enhance the safety and packet delivery services by the process of flow rule installation intelligently to that specific area only. This way of flow rule installation keeps the controller away from irrelevant areas to install rules, hence, reduces the network overhead exponentially. Proposed models are created and tested in MATLAB with real-time global positioning system logs from Jeju, South Korea.

In Internet of Vehicles (IoV), numerous routing metrics have been used to assess the performance of routing protocols such as, packet delivery ratio, throughput, end-to-end delay and path duration. Path duration is an influential design parameter, among these routing metrics, that determines the performance of vehicular networks. For instance, in highly dynamic scenarios, it can be used to predict link life time in on-demand routing protocols. In this paper, we propose an infrastructure-assisted hybrid road-aware routing protocol which is capable of enhanced vehicle-to-vehicle and vehicle-to-infrastructure communication. A remarkable aspect of the proposed protocol is that it establishes a link between path duration and fundamental design parameters like vehicular velocity, density, hop count and transmission range. Although, a lot of research has been previously performed, a well defined analytical model for IoV is not available in the literature. Precisely, a relation between path duration and vehicular velocity has not been validated in the previous studies. Experimental results show that the increased packet delivery ratio with reduced end-to-end delay can be achieved by the prediction of path duration. Proposed model for path duration is validated by getting experimental results from network simulator 3 (NS3) and analytical results from MATLAB. In addition, SUMO simulator was used to generate real time traffic on the roads of Gangnam district, South Korea.

Proposing an optimal routing protocol for internet of vehicles with reduced overhead has endured to be a challenge owing to the incompetence of the current architecture to manage flexibility and scalability. The proposed architecture, therefore, consolidates an evolving network standard named as software defined networking in internet of vehicles. Which enables it to handle highly dynamic networks in an abstract way by dividing the data plane from the control plane. Firstly, road-aware routing strategy is introduced: a performance-enhanced routing protocol designed specifically for infrastructure-assisted vehicular networks. In which roads are divided into road segments, with road side units for multi-hop communication. A unique property of the proposed protocol is that it explores the cellular network to relay control messages to and from the controller with low latency. The concept of edge controller is introduced as an operational backbone of the vehicle grid in internet of vehicles, to have a real-time vehicle topology. Last but not least, a novel mathematical model is estimated which assists primary controller in a way to find not only a shortest but a durable path. The results illustrate the significant performance of the proposed protocol in terms of availability with limited routing overhead. In addition, we also found that edge controller contributes mainly to minimizes the path failure in the network.

In the realm of software engineering, understanding the architecture and measuringthe quality of a source code is essential for maintaining and enhancing softwaresystems. However, many existing tools fall short in evaluating architectural aspects,such as detecting architectural erosion or addressing architecture-related metrics andconstraints tailored to the unique context of their systems or organisations. Thisdeficiency restricts proactive architecture governance and hinders the mitigation ofarchitecture-related risks, creating a critical gap in the analysis of software sourcecode.

This thesis presents a novel approach to tackle these challenges. It proposes a graphdatabase as a data structure for analysing the source code and architecture quality andcalculating various architectural metrics of interest. The tool developed in this thesisrepresents the source code structural elements and their relationships in the graphdatabase, enabling an intuitive analysis of the source code architecture.

The tool also integrates different code quality metrics parsed from Visual Studiocode metrics results, mapped to their correspondent nodes to assess the source codeoverall quality and identify potential areas of improvement. This empowers softwareengineers and developers to make informed decisions regarding refactoring, codeoptimisation, and architectural enhancements.

Furthermore, the tool allows users to define the intended architecture in terms ofmodules to reveal any Architecture erosion (AEr). It also provides the flexibility toestablish custom constraints and metrics through tailored queries, accommodating theunique requirements of their system and company.

A case study conducted on a real-world software project validates the effectivenessand usefulness of the proposed approach. The case study demonstrates how the toolanalysis reveals valuable insights into the source code health and identifies patternsthat can impact maintainability and scalability. The results of this research showcasethe potential of our tool as a powerful instrument for analysing the code qualityand architecture of source code, fostering more resilient and sustainable softwaresystems.

Cloud computing is gaining significant traction and virtualized data centers are becoming popular as a cost-effective infrastructure in telecommunication industry. Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) are being widely deployed and utilized by end users, including many private as well as public organizations. Despite its wide-spread acceptance, security is still the biggest threat in cloud computing environments. Users of cloud services are under constant fear of data loss, security breaches, information theft and availability issues. Recently, learning-based methods for security applications are gaining popularity in the literature with the advents in machine learning (ML) techniques. In this work, we explore applicability of two well-known machine learning approaches, which are, Artificial Neural Networks (ANN) and Support Vector Machines (SVM), to detect intrusions or anomalous behavior in the cloud environment. We have developed ML models using ANN and SVM techniques and have compared their performances. We have used UNSW-NB-15 dataset to train and test the models. In addition, we have performed feature engineering and parameter tuning to find out optimal set of features with maximum accuracy to reduce the training time and complexity of the ML models. We observe that with proper features set, SVM and ANN techniques have been able to achieve anomaly detection accuracy of 91% and 92% respectively, which is higher compared against that of the one achieved in the literature, with reduced number of features needed to train the models.

Connected vehicles can make roads traffic safer and more efficient, but require the mobile networks to handle time-critical applications. Using the MONROE mobile broadband measurement testbed we conduct a multi-access measurement study on buses. The objective is to understand what network performance connected vehicles can expect in today's mobile networks, in terms of transaction times and availability. The goal is also to understand to what extent access to several operators in parallel can improve communication performance. In our measurement experiments we repeatedly transfer warning messages from moving buses to a stationary server. We triplicate the messages and always perform three transactions in parallel over three different cellular operators. This creates a dataset with which we can compare the operators in an objective way and with which we can study the potential for multi-access. In this paper we use the triple-access dataset to evaluate single-access selection strategies, where one operator is chosen for each transaction. We show that if we have access to three operators and for each transaction choose the operator with best access technology and best signal quality then we can significantly improve availability and transaction times compared to the individual operators. The median transaction time improves with 6% compared to the best single operator and with 61% compared to the worst single operator. The 90-percentile transaction time improves with 23% compared to the best single operator and with 65% compared to the worst single operator.

In this paper we derive and discuss some newtheorems related to all rearrangementsof a given set inRn, denoted(x)and use the results to prove some new Jensen type inequalitiesfor convex, superquadratic, strongly convex and 1-quasiconvex functions.

In this paper we discuss the Hermite-Hadamard and Fejer inequalities vis-a-vis the convexity concept. In particular, we derive some new theorems and examples where Hermite-Hadamard and Fejer type inequalities are satisfied without the assumptions of convexity or concavity on the actual interval [a,b]

In this paper, we develop explicit and semi-implicit second-order high-resolution finite difference schemes for a structured coagulation-fragmentation model formulated on the space of Radon measures. We prove the convergence of each of the two schemes to the unique weak solution of the model. We perform numerical simulations to demonstrate that the second order accuracy in the Bounded-Lipschitz norm is achieved by both schemes.

Studiens syfte var att ta reda på vilka strategier lärare som använder en Montessori-inspirerad pedagogik anser stimulerar och utmanar högpresterande elever i matematik samt om de upplevde några svårigheter eller problem med att undervisa högpresterande elever i matematik. För att undersöka det har sex kvalitativa intervjuer genomförts med lärare som har varit eller som är verksamma i årskurserna 1-3. Resultatet av intervjuerna visar att lärarna har en gemensam uppfattning om att en Montessori-inspirerad pedagogik utmanar och stimulerar högpresterande elever i matematik eftersom grundtanken i Montessori-pedagogiken är "eleven i centrum". Det här innebär att lärarna måste utgå från varje elevs förutsättningar, behov och aktuella kunskapsnivå. I resultatet framkommer också att lärarna använder sig av olika strategier i form av matematikbok från en högre årskurs, special- och fördjupningsområden, åldersintegrering och lärande med flera sinnen för att stimulera och utmana de högpresterande eleverna matematiskt. Det framgår också i resultatet att lärarna inte upplever några större problem eller svårigheter i undervisningen av högpresterande elever i matematik. De belyser ändå att den egna matematiska kompetensen, tidsbrist och eget kontrollbehov ibland kan utgöra hinder för att stimulera och utmana de högpresterande eleverna tillräckligt. Utifrån studien kan man dra slutsatserna att lärare som använder en Montessori-inspirerad pedagogik lägger stor vikt vid att hitta rätt strategi för rätt elev, vilket bidrar till att de högpresterande eleverna får utmaning och stimulans. De olika hindren som lärarna belyser skulle kunna minskas med fortbildning och samarbete mellan årskurserna. 

Channel estimation is the process of understanding and analyzing the wireless communication channel's properties. It helps optimize data transmission by providing essential information for adjusting encoding and decoding parameters. This thesis explores using a Convolutional Neural Network~(CNN) for channel estimation in the 5G Link Level Simulator, 5G-LLS, developed by Tietoevry. The objectives were to create a Python framework for channel estimation experimentation and to evaluate CNN's performance compared to the conventional algorithms Least Squares~(LS), Minimum Mean Square Error~(MMSE) and Linear Minimum Mean Square Error~(LMMSE). Two distinct channel model scenarios were investigated in this study.

The results from the study suggest that CNN outperforms LMMSE, LS, and MMSE regarding Mean Squared Error~(MSE) for both channel models, with LMMSE at second place. It managed to lower to the MSE by 85\% compared to the LMMSE for the correlated channel and 78\% for the flat fading channel. In terms of the overall system-level performance, as measured by Bit-Error Rate (BER), the CNN only managed to outperform LS and MMSE. The CNN and the LMMSE yielded similar results. This was due to that the LMMSE's MSE was still good enough to demodulate the symbols for the QPSK modulation scheme correctly. 

The insights in this thesis work enables Tietoevry to implement more machine learning algorithms and further develop channel estimation in 5G telecommunications and wireless communication networks through experiments in 5G-LLS. Given that the CNN did not increase the performance of the communication system, future studies should test a broader range of channel models and consider more complex modulation schemes. Also, studying other and more advanced machine learning techniques than CNN is an avenue for future research.

We develop a Deep Reinforcement Learning (DeepRL)-based, multi-agent algorithm to efficiently control autonomous vehicles that are typically used within the context of Wireless Sensor Networks (WSNs), in order to boost application performance. As an application example, we consider wireless acoustic sensor networks where a group of speakers move inside a room. In a traditional setup, microphones cannot move autonomously and are, e.g., located at fixed positions. We claim that autonomously moving microphones improve the application performance. To control these movements, we compare simple greedy heuristics against a DeepRL solution and show that the latter achieves best application performance. As the range of audio applications is broad and each has its own (subjective) per-formance metric, we replace those application metrics by two immediately observable ones: First, quality of information (QoI), which is used to measure the quality of sensed data (e.g., audio signal strength). Second, quality of service (QoS), which is used to measure the network's performance when forwarding data (e.g., delay). In this context, we propose two multi-agent solutions (where one agent controls one microphone) and show that they perform similarly to a single-agent solution (where one agent controls all microphones and has a global knowledge). Moreover, we show via simulations and theoretical analysis how other parameters such as the number of microphones and their speed impacts performance.

The Internet of today has intermediary devices known as middleboxes that perform more functions than the normal packet forwarding function of a router. Security middleboxes are a subset of these middleboxes and face an increasingly difficult task to perform their functions correctly. These middleboxes make many assumptions about the traffic that may not hold true any longer with the advent of new protocols such as MPTCP and technologies like end-to-end encryption.

The work in this thesis focuses on security middleboxes and the challenges they face. We develop methods and solutions to help these security middleboxes continue to function correctly. In particular, we investigate the case of using MPTCP over traditional security infrastructure as well as the case of end-to-end encryption. We study how practical it is to evade a security middlebox by fragmenting and sending traffic across multiple paths using MPTCP. We then go on to propose possible solutions to detect such attacks and implement them. The potential MPTCP scenario where security middleboxes only have access to part of the traffic is also investigated and addressed. Moreover, the thesis contributes a machine learning based approach to help security middleboxes detect malware in encrypted traffic without decryption.

The evolution in networking coupled with an increasing demand to improve user experience has led to different proposals to extend the standard TCP. Multipath TCP (MPTCP) is one such extension that has the potential to overcome few inherent limitations in the standard TCP. While MPTCP's design and deployment progresses, most of the focus has been on its compatibility. The security aspect is confined to making sure that the MPTCP protocol itself offers the same security level as the standard TCP.

The topic of this thesis is to investigate the unexpected security implications raised by using MPTCP in the traditional networking environment. The Internet of today has security middle-boxes that perform traffic analysis to detect intrusions and attacks. Such middle-boxes make use of different assumptions about the traffic, e.g., traffic from a single connection always arrives along the same path. This along with many other assumptions may not be true anymore with the advent of MPTCP as traffic can be fragmented and sent over multiple paths simultaneously.

We investigate how practical it is to evade a security middle-box by fragmenting and sending traffic across multiple paths using MPTCP. Realistic attack traffic is used to evaluate such attacks against Snort IDS to show that these attacks are feasible. We then go on to propose possible solutions to detect such attacks and implement them in an MPTCP proxy. The proxy aims to extend the MPTCP performance advantages to servers that only support standard TCP, while ensuring that intrusions can be detected as before. Finally, we investigate the potential MPTCP scenario where security middle-boxes only have access to some of the traffic. We propose and implement an algorithm to perform intrusion detection in such situations and achieve a nearly 90% detection accuracy. Another contribution of this work is a tool, that converts IDS rules into equivalent attack traffic to automate the evaluation of a middle-box.

Encryption on the Internet is as pervasive as ever. This hasprotected communications and enhanced the privacy of users. Unfortu-nately, at the same time malware is also increasingly using encryptionto hide its operation. The detection of such encrypted malware is cru-cial, but the traditional detection solutions assume access to payloaddata. To overcome this limitation, such solutions employ traffic decryp-tion strategies that have severe drawbacks. This paper studies the usageof encryption for malicious and benign purposes using large datasets andproposes a machine learning based solution to detect malware using con-nection and TLS metadata without any decryption. The classification isshown to be highly accurate with high precision and recall rates by usinga small number of features. Furthermore, we consider the deployment as-pects of the solution and discuss different strategies to reduce the falsepositive rate.

Levenshtein distance is well known for its use in comparing two strings for similarity. However, the set of considered edit operations used when comparing can be reduced in a number of situations. In such cases, the application of the generic Levenshtein distance can result in degraded detection and computational performance. Other metrics in the literature enable limiting the considered edit operations to a smaller subset. However, the possibility where a difference can only result from deleted bytes is not yet explored. To this end, we propose an insert-only variation of the Levenshtein distance to enable comparison of two strings for the case in which differences occur only because of missing bytes. The proposed distance metric is named slice distance and is formally presented and its computational complexity is discussed. We also provide a discussion of the potential security applications of the slice distance.

Traditional security mechanisms such as signature basedintrusion detection systems (IDSs) attempt to find a perfect match of aset of signatures in network traffic. Such IDSs depend on the availabilityof a complete application data stream. With emerging protocols such asMultipath TCP (MPTCP), this precondition cannot be ensured, result-ing in false negatives and IDS evasion. On the other hand, if approximatesignature matching is used instead in an IDS, a potentially high numberof false positives make the detection impractical. In this paper, we showthat, by using a specially tailored partial signature matcher and knowl-edge about MPTCP semantics, the Snort3 IDS can be empowered withpartial signature detection. Additionally, we uncover the type of Snort3rules suitable for the task of partial matching. Experimental results withthese rules show a low false positive rate for benign traffic and highdetection coverage for attack traffic.

As technology becomes ubiquitous, new vulnerabilities are being discovered at a rapid rate. Security experts continuously find ways to detect attempts to exploit those vulnerabilities. The outcome is an extremely large and complex rule set used by Intrusion Detection Systems (IDSs) to detect and prevent the vulnerabilities. The rule sets have become so large that it seems infeasible to verify their precision or identify overlapping rules. This work proposes a methodology consisting of a set of tools that will make rule management easier.

Signature-based intrusion detection systems (IDSs) are commonly utilized in enterprise networks to detect and possibly block a wide variety of attacks. Their application in industrial control systems (ICSs) is also growing rapidly as modem ICSs increasingly use open standard protocols instead of proprietary. Due to an ever changing threat landscape, the rulesets used by these IDSs have grown large and there is no way to verify their precision or accuracy. Such broad and non-optimized rulesets lead to false positives and an unnecessary burden on the IDS, resulting in possible degradation of the security. This work proposes a methodology consisting of a set of tools to help optimize the IDS rulesets and make rule management easier. The work also provides attack traffic data that is expected to benefit the task of IDS assessment.

The existing network security infrastructure is not ready for future protocols such as Multipath TCP (MPTCP). The outcome is that middleboxes are configured to block such protocols. This paper studies the security risk that arises if future protocols are used over unaware infrastructures. In particular, the practicality and severity of cross-path fragmentation attacks utilizing MPTCP against the signature-matching capability of the Snort intrusion detection system (IDS) is investigated. Results reveal that the attack is realistic and opens the possibility to evade any signature-based IDS. To mitigate the attack, a solution is also proposed in the form of the MPTCP Linker tool. The work outlines the importance of MPTCP support in future network security middleboxes.

Multipath TCP (MPTCP) is a proposed extension to TCP that enables a number of performance advantages that have not been offered before. While the protocol specification is close to being finalized, there still remain some unaddressed challenges regarding the deployment and security implications of the protocol. This work attempts to tackle some of these concerns by proposing and implementing MPTCP aware security services and deploying them inside a proof of concept MPTCP proxy. The aim is to enable hosts, even those without native MPTCP support, to securely benefit from the MPTCP performance advantages. Our evaluations show that the security services that are implemented enable proper intrusion detection and prevention to thwart potential attacks as well as threshold rules to prevent denial of service (DoS) attacks.

Multipath TCP (MPTCP) is an extension to traditionalTCP that enables a number of performance advantages,which were not offered before. While the protocol specificationis close to being finalized, there still remain some concernsregarding deployability and security. This paper describes theon going work to develop a solution that will facilitate thedeployment of MPTCP. The solution will not only allow non-MPTCP capable end-hosts to benefit from MPTCP performancegains, but also help ease the network security concerns that manymiddleboxes face due to the possibility of data stream beingfragmented across multiple subflows.

Wireless local area networks (WLANs) deployment is increasing rapidly. At the same time, WLANs have become an attractive target for many potential attackers. In spite of that, the de facto standard used to implement most WLANs (IEEE 802.11) has what appear to be residual vulnerabilities related to identity spoofing. In this paper, a pragmatic study of two common attacks on the standard is conducted. These attacks are then implemented on test beds to learn attack behavior. Finally, novel attack signatures and techniques to detect these attacks are devised and implemented in a proof of concept Wireless Intrusion Detection System (WIDS).

With the advance of fifth generation (5G) networks, network density needs to grow significantly in order to meet the required capacity demands. A massive deployment of small cells may lead to a high cost for providing. ber connectivity to each node. Consequently, many small cells are expected to be connected through wireless links to the umbrella eNodeB, leading to a mesh backhaul topology. This backhaul solution will most probably be composed of high capacity point-to-point links, typically operating in the millimeter wave (mmWave) frequency band due to its massive bandwidth availability. In this paper, we propose a mathematical model that jointly solves the user association and backhaul routing problem in the aforementioned context, aiming at the energy efficiency maximization of the network. Our study considers the energy consumption of both the access and backhaul links, while taking into account the capacity constraints of all the nodes as well as the fulfillment of the service-level agreements (SLAs). Due to the high complexity of the optimal solution, we also propose an energy efficient heuristic algorithm (Joint), which solves the discussed joint problem, while inducing low complexity in the system. We numerically evaluate the algorithm performance by comparing it not only with the optimal solution but also with reference approaches under different traffic load scenarios and backhaul parameters. Our results demonstrate that Joint outperforms the state-of-the-art, while being able to find good solutions, close to optimal, in short time.

We propose derivations of several models of adsorption-convection-diffusion-type describing  transport in fractured porous media and simulate numerically some of them. As starting point, we consider a basic scenario where the tracer (i.e. the chemical substance of interest) is supposed to cross an heterogeneous porous media made of a regular part and a fissure. The fissure is in our case a straight thin layer fracture. We focus exclusively on reducing the dimension of the fracture to a line, while aiming to derive the correct limit equations and transmission conditions. We employ formal two-scale homogenization asymptotics to derive reduced effective models. The proposed reduced effective models can become useful tools for the engineering community as they can be approximated easily numerically.

Information-centric networks (ICNs) intrinsically support multipath transfer and thus have been seen as an exciting paradigm for IoT and edge computing, not least in the context of 5G mobile networks. One key to ICN’s success in these and other networks that have to support a diverse set of services over a heterogeneous network infrastructure is to schedule traffic over the available network paths efficiently. This paper presents and evaluates ZQTRTT, a multipath scheduling scheme for ICN that load balances bulk traffic over available network paths and schedules latency-sensitive, non-bulk traffic to reduce its transfer delay. A new metric called zero queueing time (ZQT) ratio estimates path load and is used to compute forwarding fractions for load balancing. In particular, the paper shows through a simulation campaign that ZQTRTT can accommodate the demands of both latency-sensitive and-insensitive traffic as well as evenly distribute traffic over available network paths. 

We present the latency-aware multipath schedulerZQTRTT that takes advantage of the multipath opportunities ininformation-centric networking. The goal of the scheduler is touse the (single) lowest latency path for transaction-oriented flows,and use multiple paths for bulk data flows. A new estimatorcalled zero queue time ratio is used for scheduling over multiplepaths. The objective is to distribute the flow over the paths sothat the zero queue time ratio is equal on the paths, that is,so that each path is ‘pushed’ equally hard by the flow withoutcreating unwanted queueing. We make an initial evaluation usingsimulation that shows that the scheduler meets our objectives.

Information-centric networking (ICN) has been introduced as a potential future networking architecture. ICN promises an architecture that makes information independent from lo- cation, application, storage, and transportation. Still, it is not without challenges. Notably, there are several outstanding issues regarding congestion control: Since ICN is more or less oblivious to the location of information, it opens up for a single application flow to have several sources, something which blurs the notion of transport flows, and makes it very difficult to employ traditional end-to-end congestion control schemes in these networks. Instead, ICN networks often make use of hop-by-hop congestion control schemes. How- ever, these schemes are also tainted with problems, e.g., several of the proposed ICN congestion controls assume fixed link capacities that are known beforehand. Since this seldom is the case, this paper evaluates the consequences in terms of latency, throughput, and link usage, variable link capacities have on a hop-by-hop congestion control scheme, such as the one employed by the Multipath-aware ICN Rate-based Congestion Control (MIRCC). The evaluation was carried out in the OMNeT++ simulator, and demonstrates how seemingly small variations in link capacity significantly deterio- rate both latency and throughput, and often result in inefficient network link usage. 

Information-centric networking (ICN) with its design around named-based forwarding and in-network caching holds great promises to become a key architecture for the future Internet. Still, despite its attractiveness, there are many open questions that need to be answered before wireless ICN becomes a reality, not least about its congestion control: Many of the proposed hop-by-hop congestion control schemes assume a fixed and known link capacity, something that rarely – if ever – holds true for wireless links. As a first step, this paper demonstrates that although these congestion control schemes are able to fairly well utilise the available wireless link capacity, they greatly fail to keep the link delay down. In fact, they essentially offer the same link delay as in the case with no hop-by-hop, only end- to-end, congestion control. Secondly, the paper shows that by complementing these congestion control schemes with an easy- to-implement, packet-train link estimator, we reduce the link delay to a level significantly lower than what is obtained with only end-to-end congestion control, while still being able to keep the link utilisation at a high level. 

Cryptocurrencies such as Bitcoin (BTC) have seen a surge in value in the recent past and appeared as a useful investment opportunity for traders. However, their short term profitability using algorithmic trading strategies remains unanswered. In this work, we focus on the short term profitability of BTC against the euro and the yen for an eight-year period using seven trading algorithms over trading periods of length 15 and 30 days. We use the classical buy and hold (BH) as a benchmark strategy. Rather surprisingly, we found that on average, the yen is more profitable than BTC and the euro; however the answer also depends on the choice of algorithm. Reservation price algorithms result in 7.5% and 10% of average returns over 15 and 30 days respectively which is the highest for all the algorithms for the three assets. For BTC, all algorithms outperform the BH strategy. We also analyze the effect of transaction fee on the profitability of algorithms for BTC and observe that for trading period of length 15 no trading strategy is profitable for BTC. For trading period of length 30, only two strategies are profitable. 

Vegetable and fruit plants facilitate around 7.5 billion people around the globe, playing a crucial role in sustaining life on the planet. The rapid increase in the use of chemicals such as fungicides and bactericides to curtail plant diseases is causing negative effects on the agro-ecosystem. The high scale prevalence of diseases in crops affects the production quantity and quality. Solving the problem of early identification/diagnosis of diseases by exploiting a quick and consistent reliable method will benefit the farmers. In this context, our research work focuses on classification and identification of tomato leaf diseases using convolutional neural network (CNN) techniques. We consider four CNN architectures, namely, VGG-16, VGG-19, ResNet, and Inception V3, and use feature extraction and parameter-tuning to identify and classify tomato leaf diseases. We test the underlying models on two datasets, a laboratory-based dataset and self-collected data from the field. We observe that all architectures perform better on the laboratory-based dataset than on field-based data, with performance on various metrics showing variance in the range 10%–15%. Inception V3 is identified as the best performing algorithm on both datasets.

The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners.

This paper explores the relationship between self-organisation andtailored roles in large agile software development teams. The casestudy examines a Swedish IT company that has introduced the roleof “Security Master” in Scrum teams. The teams are developing avery large and secure 5G solution. Twenty semi-structuredinterviews were conducted and deductively analysed. The resultsshed light on the Security Master role, its need, responsibilities andimpact on secure coding. The paper concludes with a discussion oflessons learned and recommendations for future research in thecontext of large, security-sensitive projects.

This paper explores the relationship between self-organisation and tailored roles in large agile software development teams. The case study examines a Swedish IT company that has introduced the role of "Security Master"in Scrum teams. The teams are developing a very large and secure 5G solution. Twenty semi-structured interviews were conducted and deductively analysed. The results shed light on the Security Master role, its need, responsibilities and impact on secure coding. The paper concludes with a discussion of lessons learned and recommendations for future research in the context of large, security-sensitive projects.

Reflective learning diaries can help students develop self-regulated learning (SRL), a critical skill for success. Forteachers such learning diaries can be a valuable tool for formative evaluation of SRL. However, there is limitedresearch on the use of reflective diaries in software engineering and information systems education. To address thisgap, we conducted a case study in which reflective learning diaries were used in a course. The goal of this research isto investigate the potential of using reflective diaries to formatively evaluate three key features of SRL: conceptions ofknowledge, conceptions of learning, and strategies for monitoring and regulating learning. Our findings suggest thatreflective diaries is a valuable tool for formative evaluation of SRL in software engineering education. Reflection onlearning experiences helps students develop self-awareness of their SRL skills and identify areas for improvement.Instructors can use this information to provide students with targeted feedback and support. By using reflective diariesto promote dialogic feedback, instructors can help students to develop a deeper understanding of their own learningand to become more effective self-regulated learners. The study conclude with lesson learned that provide practicalrecommendations for educators who are swiftly moving towards online teaching.

The software companies are using Agile methods and practices totackle challenges in the rapidly changing environments and increasingly com-plex software systems. However, companies developing cyber physical systems(CPS) are still infancy in the use of Agile methods and hesitate to adopt. Thissystematic literature review was conducted in order to analyze the current trendsof Agile methods use for CPS development. The search strategy resulted in 101papers, of which 15 were identified as primary studies relevant to our research.The results show growing trend of Agile processes and Scrum is widely usedreported for CPS development. The primary studies also exhibits a growinginterest in teaching Agile in embedded systems, CPS and other engineeringdegree programs. The reported challenges included synchronization of softwareand hardware development, software and hardware developers use differentvocabulary, lack of visibility and track of software releases and project progress.Additionally, lesson learned were extracted from the primary studies for guidingthe practitioners interested in adopting Agile for CPS development.