MECInOT: a multi-access edge computing and industrial internet of things emulator for the modelling and study of cybersecurity threats

In recent years, the Industrial Internet of Things (IIoT) has grown rapidly, a fact that has led to an increase in the number of cyberattacks that target this environment and the technologies that it brings together. Unfortunately, when it comes to using tools for stopping such attacks, it can be noticed that there are inherent weaknesses in this paradigm, such as limitations in computational capacity, memory and network bandwidth. Under these circumstances, the solutions used until now in conventional scenarios cannot be directly adopted by the IIoT, and so it is necessary to develop and design new ones that can effectively tackle this problem. Furthermore, these new solutions must be tested in order to verify their performance and viability, which requires testing architectures that are compatible with newly introduced IIoT topologies. With the aim of addressing these issues, this work proposes MECInOT, which is an architecture based on openLEON and capable of generating test scenarios for the IIoT environment. The performance of this architecture is validated by creating an intelligent threat detector based on tree-based algorithms, such as decision tree, random forest and other machine learning techniques. Which allows us to generate an intelligent and to demonstrate, we could generate an intelligent threat detector and demonstrate the suitability of our architecture for testing solutions in IIoT environments. In addition, by using MECInOT, we compare the performance of the different machine learning algorithms in an IIoT network. Firstly, we present the benefits of our proposal, and secondly, we describe the emulation of an IIoT environment while ensuring the repeatability of the experiments

Performance and efficiency optimization of multi-layer IoT edge architecture

Abstract. Internet of Things (IoT) has become a backbone technology that connects together various devices with diverse capabilities. It is a technology, which enables ubiquitously available digital services for end-users. IoT applications for mission-critical scenarios need strict performance indicators such as of latency, scalability, security and privacy. To fulfil these requirements, IoT also requires support from relevant enabling technologies, such as cloud, edge, virtualization and fifth generation mobile communication (5G) technologies. For Latency-critical applications and services, long routes between the traditional cloud server and end-devices (sensors /actuators) is not a feasible approach for computing at these data centres, although these traditional clouds provide very high computational and storage for current IoT system. MEC model can be used to overcome this challenge, which brings the CC computational capacity within or next on the access network base stations. However, the capacity to perform the most critical processes at the local network layer is often necessary to cope with the access network issues. Therefore, this thesis compares the two existing IoT models such as traditional cloud-IoT model, a MEC-based edge-cloud-IoT model, with proposed local edge-cloud-IoT model with respect to their performance and efficiency, using iFogSim simulator. The results consolidate our research team’s previous findings that utilizing the three-tier edge-IoT architecture, capable of optimally utilizing the computational capacity of each of the three tiers, is an effective measure to reduce energy consumption, improve end-to-end latency and minimize operational costs in latency-critical It applications

Software Defined Networks based Smart Grid Communication: A Comprehensive Survey

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.Comment: Accepte

Mobile platform-independent solutions for body sensor network interface

Body Sensor Networks (BSN) appeared as an application of Wireless Sensor Network (WSN) to medicine and biofeedback. Such networks feature smart sensors (biosensors) that capture bio-physiological parameters from people and can offer an easy way for data collection. A new BSN platform called Sensing Health with Intelligence Modularity, Mobility and Experimental Reusability (SHIMMER) presents an excellent opportunity to put the concept into practice, with suitable size and weight, while also supporting wireless communication via Bluetooth and IEEE 802.15.4 standards. BSNs also need suitable interfaces for data processing, presentation, and storage for latter retrieval, as a result one can use Bluetooth technology to communicate with several more powerful and Graphical User Interface (GUI)-enabled devices such as mobile phones or regular computers. Taking into account that people currently use mobile and smart phones, it offers a good opportunity to propose a suitable mobile system for BSN SHIMMER-based networks. This dissertation proposes a mobile system solution with different versions created to the four major smart phone platforms: Symbian, Windows Mobile, iPhone, and Android. Taking into account that, currently, iPhone does not support Java, and Java cannot match a native solution in terms of performance in other platforms such as Android or Symbian, a native approach with similar functionality must be followed. Then, four mobile applications were created, evaluated and validated, and they are ready for use

Seedemu: The Seed Internet Emulator

I studied and experimented with the idea of building an emulator for the Internet. While there are various already available options for such a task, none of them takes the emulation of the entire Internet as an important feature in mind. Those emulators and simulators can handle small-scale networks pretty well, but lacks the ability to handle large-size networks, mainly due to: - Not being able to run many nodes, or requires very powerful hardware to do so,- Lacks convenient ways to build a large emulation, and - Lacks reusability: once something is built, it is very hard to re-use them in another emulation I explored, in the context of for-education Internet emulators, different ways to overcome the above limitations. I came up with a framework that enables one to create emulation using code. The framework provides basic components of the Internet. Some examples include routers, servers, networks, Internet exchanges, autonomous systems, and DNS infrastructure. Building emulation with code means it is easy to build emulation with complex topologies since one can make use of the common control structures like loops, subroutines, and functions. The framework exploits the idea of ``layers.\u27\u27 The idea of ``\emph{layers}\u27\u27 can be seen as an analogy of the idea of ``layers\u27\u27 in image processing software, in the sense that each layer contains parts of the image (in this case, part of the emulation), and need to be ``rendered\u27\u27 to obtain the resulting image. There are two types of layers, base layers and service layers. Base layers describe the ``base\u27\u27 of the topologies, like how routers, servers, and networks are connected, how autonomous systems are peered with each other; service layers describe the high-level services on the Internet. Examples of services layers are web servers, DNS servers, ethereum nodes, and botnet nodes. No layers are tied to any other layers, meaning each layer can be individually manipulated, exported, and re-used in another emulation. One can build an entire DNS infrastructure, complete with root DNS, TLD DNS, and deploy it on any base layer, even with vastly different underlying topologies. The result of the rendered layer is a set of data structures that represents the objects in a network emulation, like host, router, and networks. These representations can then be ``compiled\u27\u27 into something that one can execute using a compiler. The main target platform of the framework is Docker. The source of the SEEDEMU project is publicly available on Github: https://github.com/seed-labs/seed-emulator

WikiSensing: A collaborative sensor management system with trust assessment for big data

Big Data for sensor networks and collaborative systems have become ever more important in the digital economy and is a focal point of technological interest while posing many noteworthy challenges. This research addresses some of the challenges in the areas of online collaboration and Big Data for sensor networks. This research demonstrates WikiSensing (www.wikisensing.org), a high performance, heterogeneous, collaborative data cloud for managing and analysis of real-time sensor data. The system is based on the Big Data architecture with comprehensive functionalities for smart city sensor data integration and analysis. The system is fully functional and served as the main data management platform for the 2013 UPLondon Hackathon. This system is unique as it introduced a novel methodology that incorporates online collaboration with sensor data. While there are other platforms available for sensor data management WikiSensing is one of the first platforms that enable online collaboration by providing services to store and query dynamic sensor information without any restriction of the type and format of sensor data. An emerging challenge of collaborative sensor systems is modelling and assessing the trustworthiness of sensors and their measurements. This is with direct relevance to WikiSensing as an open collaborative sensor data management system. Thus if the trustworthiness of the sensor data can be accurately assessed, WikiSensing will be more than just a collaborative data management system for sensor but also a platform that provides information to the users on the validity of its data. Hence this research presents a new generic framework for capturing and analysing sensor trustworthiness considering the different forms of evidence available to the user. It uses an extensible set of metrics that can represent such evidence and use Bayesian analysis to develop a trust classification model. Based on this work there are several publications and others are at the final stage of submission. Further improvement is also planned to make the platform serve as a cloud service accessible to any online user to build up a community of collaborators for smart city research.Open Acces

Large Scale Immersive Holograms with Microsoft Hololens

Inglés: This project focuses on the premise of researching whether or not a Large Scale Immersive Hologram based off a 3D model obtained via LIDAR scanner technology is viable to be deployed, currently or in future iterations, in Microsoft’s HoloLens device. For that purpose, we will be addressing how LIDAR technology works, what can be obtained from the 3D models it generates and how we can polish and optimize the resulting tridimensional mesh objects. Later on, we will implement these objects in a development environment compatible with the HoloLens device, Unity3D, and run a performance test to see how suitable and realistic the user experience results. Furthermore, we will research how near-future technologies can greatly help to enhance this experiences through future iterations of this same device