Accurate indoor radio propagation modelling is a well-known problem that researchers tend to solve in many ways. Related methods differ in terms of accuracy and complexity. The article’s contribution is a new stochastic propagation model, Universal Indoor Multicharacteristic Propagation (UIMP), which results are statistically consistent with those measured in a real environment. The model could be used to verify real- world applications due to the low computational complexity and high accuracy level compared to state-of-the-art methods. This method can be especially beneficial for the efficient modelling of complex environments shared by many users using discrete events simulations (DES) based on a series of measurements in a real environment. In the article, we describe the method and present how to establish the model. The motivation was to develop an efficient method for use in an actual application: using the Bluetooth Low Energy (BLE) signal to estimate the position of users in indoor environments – the use of the proposed UIMP model allowed for a reliable assessment of the developed functionalities. Therefore we also present how the model was used to evaluate an example, real functionality, showing that the use of simplified models may falsify the assessment of the analyzed method and may lead e.g. to incorrect implementation assumptions.

Indoor environments are a major challenge in the domain of location-based services due to the inability to use GPS. Currently, Bluetooth Low Energy has been the most commonly used technology for such services due to its low cost, low power consumption, ubiquitous availability in smartphones and the dependence of the signal strength on the distance between devices. The article proposes a system that detects the proximity between static (anchors) and moving objects, evaluates the quality of this prediction and filters out the unreliable results based on custom metrics. We define three metrics: two matrics based on RSSI and Intertial Measurement Unit (IMU) readings and one joint metric. This way the filtering is based on both, the external information (RSSI) and the internal information (IMU). To process the IMU data, we use machine learning activity recognition models (we apply feature selection and compare three models and choose the best one-Gradient Boosted Decision Trees). The proposed system is flexible and can be easily customized. The great majority of operations can be conducted directly on smartphones. The solution is easy to implement, cost-efficient and can be deployed in real-life applications (MICE industry, museums, industry).

The chapter presents an overview of the eight that are part of the European IoT Security and Privacy Projects initiative (IoT-ESP) addressing advanced concepts for end-to-end security in highly distributed, heterogeneous and dynamic IoT environments. The approaches presented are holistic and include identification and authentication, data protection and prevention against cyber-attacks at the device and system levels. The projects present architectures, concepts, methods and tools for open IoT platforms integrating evolving sensing, actuating, energy harvesting, networking and interface technologies. Platforms should provide connectivity and intelligence, actuation and control features, linkage to modular and ad-hoc cloud services, The IoT platforms used are compatible with existing international developments addressing object identity management, discovery services, virtualisation of objects, devices and infrastructures and trusted IoT approaches.

The need to adaptively manage computer systems and networks so as to offer good Quality of Service (QoS) and Quality of Experience (QoE) with secure operation at relatively low levels of energy consumption is challenged by their sheer complexity and the wide variability of the workloads. A possible way forward is through self-awareness, whereby self-measurement and self-observation, together with on-line control mechanisms, operate adaptively to attain the required performance and QoE. We survey the premises for these ideas arising from cognitive science and active networks and review recent work on self-aware computer systems and networks, including those that propose the use of software-defined networks as a means to implement these concepts. Then we provide some examples from the literature on self-aware systems to illustrate the performance gains that they can provide. Finally, we detail an example system and its working algorithms to allow the reader to understand how such a system may be implemented. Measurements showing how it can react rapidly to changing network conditions regarding QoS and security are presented. Some conclusions and suggestions for further work are listed.

A 2.3Tbps DDoS attack was recently mitigated by Amazon, which is a new record after the 2018 GitHub attack, or the famous 2016 Dyn DNS attack launched from hundreds of thousands of hijacked Internet of Things (IoT) devices. These attacks may disrupt the lives of billions of people worldwide, as we increasingly rely on the Internet. In this paper, we tackle the problem that hijacked IoT devices are often the origin of these attacks. With the goal of protecting the Internet and local networks, we propose Autopolicy: a system that automatically limits the IP traffic bandwidth-and other network resources-available to IoT devices in a particular network. We make use of the fact that devices, such as sensors, cameras, and smart home appliances, rarely need their high-speed network interfaces for normal operation. We present a simple yet flexible architecture for Autopolicy, specifying its functional blocks, message sequences, and general operation in a Software Defined Network. We present the experimental validation results, and release a prototype open source implementation.

It has been proven in research literature that the analysis of encrypted traffic with statistical analysis and machine learning can reveal the type of activities performed by a user accessing the network, thus leading to privacy risks. In particular, different types of traffic (e.g., skype, web access) can be identified by extracting time based features and using them in a classifier. Such privacy attacks are asymmetric because a limited amount of resources (e.g., machine learning algorithms) can extract information from encrypted traffic generated by cryptographic systems implemented with a significant amount of resources. To mitigate privacy risks, studies in research literature have proposed a number of techniques, but in most cases only a single technique is applied, which can lead to limited effectiveness. This paper proposes a mitigation approach for privacy risks related to the analysis of encrypted traffic which is based on the integration of three main components: (1) A machine learning component which proactively analyzes the encrypted traffic in the network to identify potential privacy threats and evaluate the effectiveness of various mitigation techniques (e.g., obfuscation), (2) a policy based component where policies are used to enforce privacy mitigation solutions in the network and (3) a network node profile component based on the Manufacturer Usage Description (MUD) standard to enable changes in the network nodes in the cases where the first two components are not effective in mitigating the privacy risks. This paper describes the different components and how they interact in a potential deployment scenario. The approach is evaluated on the public dataset ISCXVPN2016 and the results show that the privacy threat can be mitigated significantly by removing completely the identification of specific types of traffic or by decreasing the probability of their identification as in the case of VOIP by 50%, Chat by 40% and Browsing by 33%, thus reducing significantly the privacy risk.

Internet of Things is nowadays growing faster than ever before. Operators are planning or already creating dedicated networks for this type of devices. There is a need to create dedicated solutions for this type of network, especially solutions related to information security. In this article we present a mechanism of security-aware routing, which takes into account the evaluation of trust in devices and packet flows. We use trust relationships between flows and network nodes to create secure SDN paths, not ignoring also QoS and energy criteria. The system uses SDN infrastructure, enriched with Cognitive Packet Networks (CPN) mechanisms. Routing decisions are made by Random Neural Networks, trained with data fetched with Cognitive Packets. The proposed network architecture, implementing the security-by-design concept, was designed and is being implemented within the SerIoT project to demonstrate secure networks for the Internet of Things (IoT).

The concept of Security Aware Routing is not widely adopted in current networks. However, the new IoT-centric core networks give possibilities to re-open that field of research. We consider routing to be an addition to existing network security methods, especially in IoT domain. Security-aware routing incorporates the security and safety metrics to the traditional set of metrics (bandwidth, network delay, hop count, path cost, load etc.). The paper shows a new approach in which, based on the Software Defined Networks (SDN) we estimate trust relationships between nodes and flows and use them to create SDN paths, based on the Cognitive Packet Network (CPN) principle. The Random Neural Networks (RNN) supported with cognitive packets are used for making routing decisions. The proposed solution was designed and is being implemented within the SerIoT project to demonstrate secure networks for the Internet of Things (IoT).

Continuous time Markov chains (CTMC) are one of the formalisms for building models. This paper discusses OLYMP2 - a system for solving big CTMC models (exceeding $$10^9$$ states), described with a standard programming language - Java. OLYMP2 is primarily aimed at modelling of computer networks, so its formalism comes from networking concepts, like queueing systems. Using Java as a model description allows for greater flexibility in comparison to model-checker specific languages that often do not employ complete features of an object-oriented programming. Using Java also makes the parsing of models relatively fast, due to optimised Java run-time environment. Introducing dedicated compression of transition matrices allows for keeping memory usage at reasonable level even for large models.

This paper briefly reviews some recent research in Cybersecurity in Europe funded by the European Commission in areas such as mobile telephony, networked health systems, the Internet of Things. We then outline the objectives of the SerIoT Project which started in 2018 to address the security needs of fields such as Smart Cities, Smart Transportation Systems, Supply Chains and Industrial Informatics.

Gathering data from Internet of Things and management of IoT devices requires an efficient communication architecture. In this paper we analyse architectures of the scalable, sensor-oriented IoT network management system, as well as the pros and cons of introducing into it a message-oriented middleware server (message broker). We compare two architectures: with distributed buffers and with a centralized message broker. The analysis was conducted on the basis of Markov chains and discrete event simulation.

The paper presents the evaluation of the GQP algorithm in AMI networks using real map based network topologies (low, medium and high density of buildings). The algorithm was compared to the reference algorithms: CB, SBA and DP. It was shown that the GQP algorithm presents the good scalability, relatively optimal number of selected forwarders and by introducing the redundancy factor, good fault tolerance. An improvement to GQP algorithm to add a redundancy factor is proposed, what allows to execute fault tolerant broadcast transmission.

The article proposes a novel broadcast algorithm for multi-hop wireless networks. We compare three reference algorithms: Counter Based, Scalable Broadcast and Dominant Pruning, and propose a novel Global Queue Pruning method, which limits the overhead of the transmission and provides assurance of the delivery of the messages to every node in the network. The developed algorithm creates the logical topology that consists of lower number of forwarders in comparison to the previous methods, the paths are shorter, and the 100% coverage is guaranteed. This is achieved with the higher cost of propagation of the topology information in the initialisation phase.

The article discusses topological properties of multihop wireless networks which are deployed as part of the advanced metering infrastructure (AMI). It proposes a network topology generator based on a real map. The generator imports topologies of buildings from a specific world area indicated by geographical coordinates. The described generator is used for analysis of statistical properties of AMI networks' topologies in areas with different urbanization levels. The article presents examples of different areas along with their properties. The resulting topologies are compared to uniform random topologies.

The article proposes MAGANET (Map-based Generator of AMI Network Topology), new map-based topology generator, designated for Advanced Metering Infrastructure (AMI) networks as well as for the urban wireless sensor networks (WSNs). The correct, reliable representation of network topology is crucial for the correct performance evaluation on large multihop wireless networks. Most of the current work us different random topologies. In the article we describe proposed new tool and its features. The comparison of the properties of topology provided by MAGANET for three different generation modes: uniform random, grid and map based is also presented.

We present a new synchronization method for parallel discrete event simulation for wireless networks. The method merges paradigms of time-stepped and event-driven simulations to achieve reduction of messages exchanged between local processes of simulation. The method is particularly suitable for wireless network simulation, in which objects share the same physical medium and parallel simulation involves a significant overhead for interprocess communication. The article describes the method and compare it to conservative and optimistic synchronization methods, on the basis of results of simulations of selected wireless network model.

This work concerns the problem of preparation of the computer system to represent the 3D scene in a dynamic network environment. The article presents preliminary results of network measurement of 3D mesh progressive transmission using TCP protocol. The objective is to evaluate the possible impact of transmission dynamics on the amount of data available in VR browsing client, which relates to browsing Quality of Experience. The article compares results obtained in a real network to the simulation studies, conducted previously. In four sections, the article presents a brief introduction and exploration model, simulation results, implementation of progression and simple network protocol and finally conclusions and future works.

Based on the physical layer model, developed and adopted for discrete event simulators, the preliminary evaluations of the effectiveness of the model, considering the accuracy of the simulation, are presented. We review role of the propagation model, interference model and importance of the mobility of the nodes. We believe that it is important to support decisions of choosing relevant model in discrete events simulations (DES). Such research are important especially in parallel simulations (PDES), because high memory consuming simulations, on the one hand, may lead to high communication overhead and, on the other hand, may be impossible within resources available on one computational node, and therefore may have to be distributed over parallel environment. We assume, that properly developed model (of chosen PHY aspects) in combination with dedicated synchronization method could have strong impact on the performance of the parallel simulation. In the paper we present results of selected efficiency analysis.